In moving base transfer alignment under nonlinear and non-Gaussian situation, using Kalman Filtering could cause large error or even divergence.
在非线性、非高斯条件下进行动基座传递对准，如果采用卡尔曼滤波会出现误差较大甚至发散的问题。
The particle filtering is a nonlinear filtering technology, which is suitable for the nonlinear system and non-Gaussian noise model.
粒子滤波技术是近几年出现的一种非线性滤波技术，它适用于非线性系统以及非高斯噪声模型。
It could be directly applied to the nonlinear model of the initial system, and could get good filtering result whether the system noise or measured noise was Gaussian or not.
该算法可以直接应用于原系统的非线性模型当中，并且不需考虑系统噪声和量测噪声是否为高斯白噪声，都能得到很好的滤波效果。
In Chapter 2, we present nonlinear filtration theory, and find out the nonlinear conditional Gaussian filtering estimation under one dim and multi dims.
第二章引入非线性滤波理论，给出一类关于条件高斯过程的一维和多维非线性滤波估计。
In Chapter 2, we present nonlinear filtration theory, and find out the nonlinear conditional Gaussian filtering estimation under one dim and multi dims.
第二章引入非线性滤波理论，给出一类关于条件高斯过程的一维和多维非线性滤波估计。
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